Evolutionary rank aggregation for recommender systems

Oliveira, Samuel; Diniz, Victor; Lacerda, Anísio; Pappa, Gisele L.

doi:10.1109/cec.2016.7743803

Cited by 10 publications

(2 citation statements)

References 28 publications

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“…For example, in the publication [29], the authors proposed a hybridization technique that combines recommendations generated by different recommendation algorithms, using an evolutionary algorithm for multi-criteria optimization. The publication [30] suggested an Evolutionary Rank Aggregation (ERA) algorithm that used genetic programming to directly optimize the MAP measure. The authors tested the suggested solution on four datasets, and the results clearly indicate that the technique improved the quality of the generated recommendations.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Rank Aggregation Methods in Recommendation Systems

Bałchanowski

Boryczka

2023

Entropy

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The aim of a recommender system is to suggest to the user certain products or services that most likely will interest them. Within the context of personalized recommender systems, a number of algorithms have been suggested to generate a ranking of items tailored to individual user preferences. However, these algorithms do not generate identical recommendations, and for this reason it has been suggested in the literature that the results of these algorithms can be combined using aggregation techniques, hoping that this will translate into an improvement in the quality of the final recommendation. In order to see which of these techniques increase the quality of recommendations to the greatest extent, the authors of this publication conducted experiments in which they considered five recommendation algorithms and 20 aggregation methods. The research was carried out on the popular and publicly available MovieLens 100k and MovieLens 1M datasets, and the results were confirmed by statistical tests.

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Section: Related Workmentioning

confidence: 99%

A Comparative Study of Rank Aggregation Methods in Recommendation Systems

Bałchanowski

Boryczka

2023

Entropy

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“…In the work [25], the authors suggested creating a multi-criteria recommendation system, which, in addition to the quality of the generated recommendations, also took into account measures, such as novelty and diversity. In [26], the authors used genetic programming to create a recommendation system that generated recommendations by optimizing the MAP measure. It is also worth paying attention to [7], in which the researchers asked themselves whether the problem of rank aggregation in the context of recommendation systems is worth looking into.…”

Section: Literature Overviewmentioning

confidence: 99%

Aggregation of Rankings Using Metaheuristics in Recommendation Systems

Bałchanowski

Boryczka

2022

Electronics

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Recommendation systems are a powerful tool that is an integral part of a great many websites. Most often, recommendations are presented in the form of a list that is generated by using various recommendation methods. Typically, however, these methods do not generate identical recommendations, and their effectiveness varies between users. In order to solve this problem, the application of aggregation techniques was suggested, the aim of which is to combine several lists into one, which, in theory, should improve the overall quality of the generated recommendations. For this reason, we suggest using the Differential Evolution algorithm, the aim of which will be to aggregate individual lists generated by the recommendation algorithms and to create a single list that will be fine-tuned to the user’s preferences. Additionally, based on our previous research, we present suggestions to speed up this process.

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Learning to Rank in Session-Based Recommender Systems

Ravanmehr,

Mohamadrezaei

2023

Session-Based Recommender Systems Using Deep Learning

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Evolutionary rank aggregation for recommender systems

Cited by 10 publications

References 28 publications

A Comparative Study of Rank Aggregation Methods in Recommendation Systems

A Comparative Study of Rank Aggregation Methods in Recommendation Systems

Aggregation of Rankings Using Metaheuristics in Recommendation Systems

Learning to Rank in Session-Based Recommender Systems

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